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  • Thomas Laffont of Coatue on the $4 Trillion AI IPO Wave: SpaceX, Anthropic, OpenAI, and Why the New Unicorn Economy Is Healthier

    Thomas Laffont, co-founder of the $55 billion hedge fund Coatue Management, made his All-In Podcast premiere with a data-dense walk through what he calls a once-in-a-generation moment for the unicorn economy. In front of Chamath Palihapitiya, Jason Calacanis, David Sacks, and David Friedberg, he argued that a roughly $4 trillion wave of private value is about to hit the public markets, led by SpaceX, Anthropic, and OpenAI, and that the new AI-driven unicorn economy is actually healthier than the one that came before it. You can watch the full presentation and Q&A on YouTube.

    TLDW

    Laffont presents Coatue’s slide deck on the state of the unicorn economy and argues it has rebalanced after the excesses of 2021. The average unicorn is up about 70 percent since September 2024, AI keeps taking a bigger share of all fundraising, and the model has shifted from many small unicorns to fewer companies each raising far more, with funding per unicorn up roughly 5x since 2021. He introduces a “Magnificent 8” private index (SpaceX, Stripe, Anthropic, Databricks, Revolut, ByteDance, Anduril, and more) worth nearly $4 trillion that has crushed the public Mag 7, then shows that exits are finally thawing as SpaceX heads to an IPO in weeks and Anthropic confidentially files its S1. He lays out Coatue’s “CODE” framework for why SpaceX gets more valuable the more it launches, a counterintuitive finding that the odds of a 10x actually rise as companies get bigger (31 percent for $100 billion-plus centicorns), the explosive revenue ramp of OpenAI and Anthropic past Workday, ServiceNow, Adobe, Salesforce, and now the hyperscalers, a three-pillar map of where AI revenue comes from (consumer, ads, enterprise), and the AI memory thesis. The Q&A with Chamath and Calacanis digs into the power law, K-shaped outcomes, whether these valuations are disconnected from reality, the public market as the great antiseptic, and what happens when trillions in private value finally recycles back through GPs and LPs.

    Thoughts

    The most useful idea in the talk is not the $4 trillion headline, it is the cohort-health chart. Laffont splits unicorns into eras and shows that the pre-2021 cohort was healthy, roughly 80 percent had raised again or exited 20 quarters after minting, while the giant 2021 ZIRP cohort of 479 companies is stuck with under 20 percent doing either. That single comparison reframes the whole AI boom. The bullish read is that the 2024 AI cohort is small, concentrated, and cash-generative, so it looks more like the healthy pre-ZIRP group than the 2021 hangover. The bearish read is that we are watching the same movie with bigger numbers, and the test only comes when these companies face public markets. Laffont is honest that we do not yet know which cohort the AI class resembles, and that intellectual humility is what makes the deck credible rather than promotional.

    The SpaceX “CODE” framework is the sharpest analytical move of the presentation. Most people would assume a launch business gets cheaper per launch as it scales. Laffont shows the opposite, the market pays more per launch as cadence rises, and explains it as a phase change in business quality: from one-time government launch revenue, to a single recurring-revenue constellation, to multiple constellations, to a platform with optional upside in space data centers, the moon, and Mars. It is a clean way to think about any company that climbs from a project business to a platform business, and it applies far beyond rockets. The lesson for investors is that valuation can rationally expand even as unit economics look like they should compress, because the nature of the revenue underneath is changing.

    The counterintuitive 10x odds finding deserves more attention than it got in the room. Conventional wisdom says the bigger you are, the harder it is to grow, so a $100 billion company should be less likely to 10x than a $10 billion one. Coatue’s data says the reverse: centicorns have a 31 percent shot at a 10x, far higher than the 8 percent a unicorn has at becoming a decacorn. Laffont’s explanation is a filtering mechanism, every step up validates a compounding advantage and durability of earnings, so survivors are increasingly the kind of business that keeps compounding. This is essentially a quantitative restatement of quality investing, and it is the intellectual backbone of the LP strategy the besties tease out, just buy whoever reaches $100 billion and hold.

    Where the argument gets genuinely contested is valuation, and the panel does not let it slide. The pushback that “these are not fake companies” is true and important, OpenAI and Anthropic are growing faster than any software company in history, and Anthropic reportedly had a profitable month. But growth and reality do not settle the question of price when you are paying 50 to 100 times revenue for trillion-dollar private companies, as Bill Ackman pointed out earlier in the day. Laffont’s answer is the most grounded thing he says all session: the public market is the great antiseptic, it will not care about anyone’s slide deck, and he wants to see these names withstand short sellers and skeptics. That is the right posture. The deck is a thesis, not a verdict, and the verdict arrives roughly six months and one day after the IPOs, once passive flows and supply have washed through.

    The closing thread, that almost every sector is being transformed at once and we still do not have superintelligence, is the part worth sitting with. The risk in a presentation this bullish is treating the trend as destiny. The value is in the framing tools Laffont hands you, cohort health, phase-change business quality, the filtering odds, the three revenue pillars, and the antiseptic of public scrutiny. Use those to interrogate each name rather than to buy the index on faith, and the talk earns its premiere billing.

    Key Takeaways

    • Coatue Management is one of the most successful hedge funds of the last two decades with about $55 billion under management, and is raising roughly another billion dollars specifically to invest in AI.
    • The unicorn economy is up about 70 percent on average since September 2024, and the public market has made a similar move up over the same period.
    • The unicorn economy’s share of the NASDAQ rose significantly after 2015 but has plateaued in recent years, reflecting strong performance from public companies.
    • AI keeps increasing its wallet share of all venture fundraising, multiple years in a row now.
    • The composition of funding has changed. The unicorn “factory” peaked in the ZIRP era of 2021 and has normalized at a much lower level since.
    • Funding per unicorn has increased roughly 5x since 2021. There are fewer unicorns, and each one is raising more.
    • Cohort health, pre-ZIRP group: of about 73 unicorns, 20 quarters after minting roughly 80 percent had either raised a new round or exited, which is healthy.
    • Cohort health, 2021 group: of about 479 unicorns, 20 quarters in, fewer than 20 percent had exited or raised again. Far larger cohort, far worse outcomes.
    • The open question is which cohort the new 2024 AI cohort will resemble.
    • Funding is concentrating: the top 10 companies capture a large share, and it is a small number of AI companies, not all of them, with Anthropic and OpenAI raising massive rounds.
    • Laffont proposes a “Magnificent 8” private index: SpaceX, Stripe, Anthropic, Databricks, Revolut, ByteDance, Anduril, and more, spanning internet, AI, fintech, and space tech.
    • That private index represents almost $4 trillion of value and has crushed the traditional public Mag 7, with almost every name outperforming.
    • Exits are thawing. 2026 is on a good trend for cash returned versus consumed, not quite 2021 levels, with half a year still to go.
    • That trend does not yet include three imminent liquidity events: SpaceX (IPO expected in weeks) and Anthropic (confidentially filed its S1), whose combined value could exceed the prior decade of exits combined.
    • The ecosystem is far more balanced than when Laffont first presented at the 2024 All-In Summit, when it was consuming much more cash than it returned.
    • OpenAI and Anthropic revenue growth is unlike anything previously seen. Starting from January 2025, they passed Workday, then ServiceNow, then Adobe, then Salesforce, and are now bigger than Google Cloud and Azure.
    • On current forecasts, that revenue could pass AWS by the end of the year and exceed all of Microsoft by 2028.
    • Hyperscalers are not sitting still. The largest companies in the world are funding the disruption, investing unprecedented sums to enable the ChatGPT moment.
    • The SpaceX “CODE” framework: the number one driver correlated to SpaceX’s valuation is cadence of launches, and valuation per launch rises as launches increase.
    • Why per-launch value rises: business quality improves through phases, pre-constellation (one-time government revenue), initial ramp (one recurring-revenue constellation), scale (multiple constellations), and platform (space data centers, moon and Mars optionality).
    • Anthropic in particular is scaling like no company seen across the PC, internet, or mobile eras.
    • Counterintuitive 10x odds: a unicorn has about an 8 percent chance of becoming a decacorn, a decacorn has 8 to 13 percent odds of reaching $100 billion, but a centicorn ($100 billion-plus) has a 31 percent chance of a 10x.
    • Value creation has accelerated. It typically takes years to go from $500 billion to $1 trillion in market cap, yet recently three companies did it in one year and two did it in a matter of weeks.
    • Cerebras is the counterexample of slow success: years of dark periods and no new capital developing its technology, then a massive OpenAI contract that quintupled the company’s value ahead of its IPO.
    • Semiconductors are on a generational run, with the sector dramatically outperforming the index since the 2024 All-In Summit.
    • AI memory thesis: the more an AI system knows about you, the more useful it is, so memory per user could quintuple, which helps explain recent moves in memory companies.
    • Where the revenue is: the AI ecosystem is roughly $140 billion today, about $300 billion this year, and is expected to double in 2027.
    • Three revenue pillars: consumer (subscribers times ARPU), ads (about a quarter of Meta and Google ads are AI-enabled today, heading toward 100 percent and roughly $150 billion), and enterprise (tools like Claude Code and Codex inside businesses).
    • Disruption is hitting every sector: software, telco (Starlink-powered global phone calls), semis, energy (data centers reshaping Pennsylvania’s grid), auto (Ferrari’s electric and autonomous stumble), and consumer (GLP-1s reshaping food, alcohol, and wellness).
    • Final takeaways: the new unicorn economy is healthier thanks to AI, winners are compounding faster so the cost of not owning a winner is higher than ever, disruption is everywhere, and we do not even have superintelligence yet.
    • In the Q&A, both Anthropic and OpenAI publicly say they want to be public, and big outcomes now look likely to become liquid within roughly a 12-month window.
    • The valuation pushback: these are not fake companies, they generate substantial revenue at scale and grow faster than anything before, and Anthropic reportedly even had a profitable month.
    • The public market is framed as the great equalizer and antiseptic, but with passive buying the true price discovery may not land on day one, more like six months and a day after listing.
    • A floated LP strategy: wait for whoever reaches $100 billion and concentrate capital there as the least brittle, quickest-return bet, tempered by the warning that valuations are disconnecting from any historical metric (50x to 100x revenue).
    • An open risk: with so much capital, OpenAI and Anthropic could rationally start a price war, the way ride-sharing and food-delivery players once did, though heavy infrastructure spend complicates it.

    Detailed Summary

    The unicorn economy has rebalanced after 2021

    Laffont opens by reframing a market many assume is frothy. The average unicorn is up about 70 percent since September 2024, and the public market has tracked a similar climb, so private and public value are moving together rather than diverging. The unicorn economy’s share of the NASDAQ rose sharply after 2015 and then plateaued, which he reads as a sign of how strong public companies have become. Underneath the headline, the structure of funding has changed. The 2021 ZIRP era was a unicorn factory that minted enormous numbers of companies, and that machine has since normalized to a much lower level. The result is a barbell: fewer new unicorns, but each raising far more, with funding per unicorn up roughly 5x since 2021. AI sits at the center of this, taking a steadily larger share of all venture dollars for several years running.

    Cohort health is the real story

    The deck’s most important slide measures the health of the ecosystem by cohort. The pre-ZIRP cohort, about 73 unicorns, looks healthy: 20 quarters after becoming unicorns, roughly 80 percent had either raised a new round or exited. The 2021 cohort tells the opposite story. It is enormous, about 479 unicorns, and 20 quarters in, fewer than 20 percent had raised again or exited. That contrast sets up the central question of the talk. A new 2024 cohort of AI companies is forming, and no one yet knows whether it will resemble the healthy pre-ZIRP group or the bloated, stuck 2021 group. Laffont’s framing leans optimistic because the AI cohort is small and concentrated, but he is careful not to declare the answer.

    The Magnificent 8 and a $4 trillion private index

    Funding is not just flowing to AI, it is flowing to a handful of AI names, with the top 10 capturing a large share and Anthropic and OpenAI raising the biggest rounds. From this concentration Laffont builds a private index he half-jokingly calls the Magnificent 8, a number he expects to shrink as companies go public. The members span sectors: SpaceX, Stripe, Anthropic, Databricks, Revolut, ByteDance, and Anduril, covering internet, AI, fintech, and space tech. He says he would be comfortable owning that index for the next decade-plus. Collectively it represents almost $4 trillion of value and has outperformed the public Mag 7, with nearly every constituent beating that benchmark.

    Exits are thawing and a wall of liquidity is coming

    One of Laffont’s recurring concerns at past summits has been balance: the unicorn economy is great at consuming cash, but a healthy ecosystem must also return it. On that score 2026 is trending well, not quite 2021, but solid with half a year left. Crucially, that figure does not yet include three imminent events. SpaceX is expected to go public within weeks, and Anthropic confidentially filed its S1 the day of the talk. Adding those up, just a few companies could deliver more liquidity than the prior ten years combined. The takeaway is that the ecosystem that was dangerously out of balance in 2024 is now meaningfully more balanced, and improving.

    The revenue ramp past the hyperscalers

    The growth rates of OpenAI and Anthropic, Laffont argues, are unlike anything previously seen. Charting from January 2025, the leading AI labs passed Workday, then ServiceNow, then Adobe by year end, then Salesforce by January, and are now bigger than Google Cloud and Azure. On forecast, that revenue could surpass AWS by the end of the year and exceed all of Microsoft by 2028. He stresses that the hyperscalers are not passive bystanders, they are actively funding the disruption, pouring unprecedented capital into enabling the change that began with the ChatGPT moment.

    The SpaceX CODE framework

    Laffont devotes real time to how Coatue thinks about SpaceX. The single factor most correlated with SpaceX’s valuation is cadence of launches, which is intuitive for a launch business. The surprise is that valuation per launch has risen rather than fallen as cadence climbed. His explanation, the CODE framework, is that the quality of the business model improves the more SpaceX launches. In phase one, pre-constellation, you are simply proving rockets, with a few government customers and lumpy, unpredictable one-time revenue. In the initial ramp you stand up a constellation, which is an end market and a recurring-revenue business that grows with every satellite and subscriber. At scale you operate multiple constellations, and Laffont expects companies, governments, and militaries to want to own their own. Ultimately it becomes a platform, with new businesses layered on top, from space data centers to the optionality of the moon and Mars.

    Counterintuitive odds and the speed of value creation

    Coatue bucketed companies and asked the odds of a 10x within each. A unicorn has roughly an 8 percent chance of becoming a decacorn. A decacorn has 8 to 13 percent odds of reaching $100 billion. But a centicorn, $100 billion or more, has a 31 percent chance of a 10x, counting both public and private companies. The bigger you are, the better your odds, which inverts intuition. Laffont pairs this with the sheer speed of recent value creation. Going from $500 billion to $1 trillion in market cap normally takes years, yet three companies did it in a single year and two did it in a matter of weeks. He also offers Cerebras as the patient counterexample, a chip company that endured years of dark periods and no new capital before a massive OpenAI contract quintupled its value ahead of IPO, part of a broader generational run for semiconductors.

    AI memory and where the revenue actually comes from

    A throughline from the day’s other speakers is that the more an AI knows about you, the more useful it is, from your restaurant preferences to your work context. Laffont turns that into a thesis: memory per user could quintuple based on what these systems require, which helps explain recent moves in memory companies. He then tackles the most contested question, where is the revenue. He sizes the AI ecosystem at about $140 billion today, roughly $300 billion this year, and doubling in 2027, built on three pillars. Consumer is subscribers times ARPU. Ads are the pillar people forget, with about a quarter of Meta and Google ads already AI-enabled and penetration heading toward 100 percent, a roughly $150 billion opportunity. Enterprise is the breakthrough category, exemplified by tools like Claude Code and Codex operating inside businesses.

    Every sector is being transformed at once

    What makes this era different, Laffont says, is that nearly every sector is being transformed simultaneously. Software is obvious, but look at telco, where he believes Starlink will soon power a device that lets you make a phone call anywhere on earth, attacking the global telco and broadband profit pool with a better product. Compute is driving massive change in semis, data centers are reshaping the energy equation in places like Pennsylvania, and the auto business is being upended, as Ferrari’s stumble introducing electric and autonomous technology showed. In consumer, GLP-1 drugs are profoundly changing consumption of food and alcohol and the broader focus on wellness. His takeaways close the loop: the new unicorn economy is healthier thanks to AI, winners are compounding faster so the cost of missing them is higher than ever, disruption is everywhere, and superintelligence has not even arrived yet.

    The Q&A: power law, valuation, and the public market test

    Chamath and Jason Calacanis press Laffont on what this means for allocators. The recurring theme is the power law and K-shaped outcomes, with gains consolidating into a small number of companies. The positive side, Laffont notes, is that outcomes are enormous and increasingly liquid within a 12-month window, and both Anthropic and OpenAI say they want to be public. The hard part is valuation. The besties cite Bill Ackman’s framing that investors are making venture bets on trillion-dollar companies at 50 to 100 times revenue. Laffont’s pushback is that these are not fake companies, they generate substantial revenue at scale and grow faster than anything before, and Anthropic reportedly had a profitable month. But he embraces the discipline ahead: the public market is the great antiseptic and will not care about anyone’s presentation, though with heavy passive buying, true price discovery may take roughly six months and a day rather than landing on day one. Asked whether the compounding is a market inefficiency or survivor bias, he declines to over-read a small sample, noting that Anthropic before Claude Code was a completely different company than after. The conversation closes on what happens when trillions recycle from GPs to LPs, the case for simply owning whoever crosses $100 billion, the risk of everyone crowding into three names, and the possibility of an eventual OpenAI versus Anthropic price war.

    Notable Quotes

    “So we have fewer unicorns that are each raising more.”

    Thomas Laffont, summarizing how funding per unicorn has risen roughly 5x since 2021

    “The reason is that the quality of SpaceX’s business model increases the more you launch.”

    Thomas Laffont, explaining the CODE framework and why valuation per launch rises with cadence

    “The winners are compounding faster than ever, which means the costs of not being in a winner are higher than ever.”

    Thomas Laffont, on the central risk of a power-law market

    “And by the way, we don’t even have super intelligence yet.”

    Thomas Laffont, closing his takeaways on how early the transformation still is

    “These are companies generating substantial revenue at scale that are growing faster than anything we’ve ever seen.”

    Thomas Laffont, pushing back on the idea that AI valuations rest on fake companies

    “It will be the great antiseptic. It will not care about my presentation.”

    Thomas Laffont, on the public market as the ultimate test for SpaceX, OpenAI, and Anthropic

    “Anthropic pre-cloud code was a completely different company than post cloud code.”

    Thomas Laffont, on why he won’t over-read a small sample of hyper-compounders

    “The power law rules our lives. All the great gains are being consolidated into small numbers of companies.”

    An All-In host, framing the Q&A on concentration in private markets

    This is a curated set of highlights. To hear the full presentation, the slide walkthrough, and the complete Q&A with Chamath and Jason Calacanis, watch the full conversation here.

    Related Reading

    • Coatue Management. Primary source for Thomas Laffont’s firm and the technology investing strategy behind the deck.
    • The All-In Podcast. The show and summit where Laffont made this premiere presentation.
    • Power law (Wikipedia). Background on the distribution Laffont and the hosts say governs venture and public-market returns.
    • The Magnificent Seven (Wikipedia). The public-market benchmark Laffont’s private “Magnificent 8” index is measured against.
    • Cerebras Systems. The AI chipmaker Laffont cites as the slow-grind IPO that was eventually transformed by a major OpenAI contract.

  • Bill Ackman on Investment Strategy, What the Market Is Missing, and How AI Breaks Businesses

    Bill Ackman, founder and CEO of Pershing Square, joined the All-In Podcast for a conversation about how his investment approach has shifted toward permanent, long-term ownership, why he believes the highest-quality companies are being left behind by a market chasing the new new thing, and how AI is raising the risk of disruption for almost every business. He also lays out his plan to turn Howard Hughes into a Berkshire Hathaway-style compounding machine built on insurance. You can watch the full conversation here. Below is a structured breakdown of the ideas, the stories, and the frameworks he uses to underwrite a business.

    TLDW

    Ackman explains how his philosophy evolved from a smaller, more liquid activist toward concentrated, permanent ownership of durable, non-disruptible businesses, with much of his activism now playing out on X rather than in the boardroom. He tells the origin story of his first big trade, Wendy’s and the Tim Hortons spin-off, and explains why a large long-term shareholder on a board is an antidote to short-term markets. On AI, he argues that this is the greatest era in history to build a company, which means the risk of being disrupted has gone up enormously, and that the market is mispricing high-quality compounders like Microsoft, Meta, and Amazon while crowding into chips, semiconductors, and energy. He works through the SaaS question and why niche software is more at risk than platforms, how he underwrites SpaceX, xAI, OpenAI, Anthropic, and Palantir like late-stage venture bets using a people, opportunity, context, deal framework, and why founder-led companies have an edge in making radical calls. The back half covers his Howard Hughes plan to copy Buffett’s insurance-float model, the role of cost of capital and reflexivity in markets, the meme-stock era, going direct on social media, and the three different ways an investor can put money to work with Pershing Square.

    Thoughts

    The most useful idea in the interview is the way Ackman reframes disruption as the central investing problem of the AI era. His point is that the same forces making this the best time in history to start a company, meaning near-unlimited compute, capital, and talent, also raise the odds that any given incumbent gets disrupted. That reframes the word quality. It is no longer mostly about margins and moats. It becomes about non-disruptibility, which is a much higher bar than most quality investors were using a decade ago, and it is why he says most of his research time now goes into assessing that single risk.

    The what-the-market-is-missing thesis is classic contrarian Ackman. Arguing that Microsoft, Meta, and Amazon are the new old-fashioned, undervalued names while capital piles into semiconductors and energy is a direct echo of 2000, when Berkshire Hathaway bottomed precisely because money was chasing internet stocks. It is worth keeping in mind that he owns all three, so the call is also his book. The durable signal here is the framework, not the specific tickers: capital reliably chases the new new thing, and genuinely high-quality businesses get left behind during those rotations.

    The Howard Hughes plan is the most concrete bet in the conversation. Copying Buffett’s insurance-float playbook, short-term treasuries for policyholder money and equities for the surplus, onto a discounted real-estate holding company is elegant. The hard part is exactly what Ackman flags about insurance as an industry: the best investors go to hedge funds, not insurers, so most insurance companies only ever manage the liability side well. Pershing Square’s edge is that Ackman can both write the business and invest the float, which is the same reason it worked for Buffett. The framing of going from a four billion dollar company to a trillion over fifty years is a statement of intent, not a forecast, and should be read that way.

    Underneath all of it sits cost of capital and reflexivity. His observation that a higher stock price literally makes a company more valuable, because it lowers the cost of capital and creates acquisition currency, is the mechanism behind both Elon Musk’s empire and the meme-stock era he is wary of. Going direct on X is the same lever pointed at himself: communicate the vision, lower your own cost of capital, and make the bet easier for other people to place. It is a coherent worldview in which narrative and balance sheet continuously feed each other, and it explains a lot of his behavior over the last few years.

    Key Takeaways

    • The biggest change in Ackman’s approach over time is an appreciation for business quality, meaning long-term, durable, protected, non-disruptible growth as the most important factor.
    • He says he is as activist as ever, but more of it now happens on X than in the traditional corporate context.
    • His first big investment was Wendy’s, which owned Tim Hortons. The simple thesis was to buy Wendy’s, spin off Tim Hortons, and double the money.
    • Early on no one returned his calls, so he had Steve Schwarzman’s Blackstone write a fairness opinion, filed it publicly, and the company spun off Tim Hortons six weeks later. The CEO later thanked him after being fired with a large exit package.
    • Reputation compounds. Where Pershing Square once had to bang down the door, companies now sometimes tweet a welcome when it buys a stake.
    • A large long-term shareholder on a board is a counterweight to short-term markets, letting management test ideas privately and pursue initiatives that hurt the next few quarters of earnings.
    • Pershing Square owns Microsoft, Meta, and Amazon. Ackman argues you are either invested in AI directly or indirectly, or it is a threat, so you have to understand it.
    • The hardest and most important job for a concentrated investor is judging the risk of disruption, and that risk has risen dramatically.
    • This is the greatest era in history to build a business because of near-unlimited access to compute, capital, and talent, which is exactly why the probability of being disrupted has gone up enormously.
    • Markets bring their eye to the new new thing, currently chips, semiconductors, and energy, while high-quality companies get left behind.
    • He draws an analogy to 2000, when Berkshire Hathaway traded at one of its lowest valuations because everyone chased internet stocks. He sees a similar dynamic around Amazon, Meta, and Microsoft today.
    • On the SaaS question, he worries more about a Salesforce than a platform like Microsoft, because niche software charging high per-seat or per-year prices is most exposed, while low-priced platforms are safer.
    • Any software company today has to be as AI-enabled as possible, or risk losing the monopolistic pricing it once enjoyed.
    • His famous March 2020 CNBC appearance was an attempt to reach President Trump and argue for a short shutdown, paired with the view that stocks were incredibly cheap and worth buying.
    • He describes valuation as a tether on the market: when prices stretch too high they snap back, and when they get too cheap the same rubber band pulls valuations up. Calling that out publicly can trigger a psychological reset.
    • His recent bullish call came because stocks of really high-quality companies had gotten crazy cheap on fundamentals, meaning the present value of the cash they generate.
    • He underwrites high-multiple names like SpaceX as venture investments using a framework from business school: people, opportunity, context, deal.
    • On SpaceX, people and opportunity are one of one, the context is incredible, and Starlink plus near-monopoly low-cost launch make it strategically valuable. The complicated part is the deal, meaning the valuation. He invested via an SPV after Ron Baron’s nudge, and also invested in xAI.
    • He treats OpenAI, Anthropic, and Palantir as late-stage venture bets that have proven they can generate real revenue, and says OpenAI should do a better job communicating how it thinks about its enormous capital commitments.
    • Every CEO in America is asking how to use AI, how it applies to their business, and how it is a threat. It is top of mind and boards open every meeting with it.
    • He has not seen much enterprise AI success yet, citing a McKinsey study that 95 percent of enterprise initiatives fail and the rise of the forward deployed engineer as the hot role bridging promise and ROI. Pershing Square itself uses AI mainly for legal, compliance, and back-office work.
    • Founder-led companies have an advantage because founders have the authority and the economic stake to make radical calls, while the average S&P 500 CEO has a roughly three to four year tenure and is incentivized not to make mistakes.
    • He cites Mark Zuckerberg buying Instagram and WhatsApp as the kind of shocking-at-the-time calls that a founder with a track record can make.
    • Ben Graham’s enduring lesson is that a stock is an interest in a business, not a piece of paper, but Graham mostly invested in liquidations and cash-rich shells, and made most of his money on Geico.
    • Most of Buffett’s value at Berkshire came from owning insurance operations and focusing on the asset side of the balance sheet, not just the liability side.
    • Insurance is hard to copy because top investors do not go to work for insurers. Buffett owned half his company and was a great investor, which is why it worked.
    • Howard Hughes came out of the General Growth bankruptcy and owns master-planned cities like Summerlin, with 26,000 acres in the Las Vegas area, comparable to the Irvine Company that built roughly a hundred billion dollars of wealth for Donald Bren.
    • The plan is to reinvest the cash Howard Hughes generates into insurance, put policyholder float in short-term treasuries and the surplus in common stocks, and build a compounding machine over fifty years, buying it at roughly sixty cents on the dollar.
    • A company must earn a return above its cost of capital for the stock to rise. Elon Musk has kept his companies’ cost of capital extremely low, and a SpaceX IPO near a 1.75 trillion dollar valuation could be one of the lowest cost of equity capital transactions ever.
    • Markets have changed less because of Ackman and more because of figures like Ryan Cohen and GameStop, where a stock can trade well above its value on personality and an army of followers.
    • Higher valuations are reflexive: a rising stock price lowers cost of capital and creates currency to issue stock and acquire businesses, which is part of how Elon built Tesla.
    • There are three ways to invest with Pershing Square: the management company itself (a royalty on compounding assets with no capex), PSUS (a portfolio of best ideas trading at an 18 percent discount), and Howard Hughes (a bet on building the next Berkshire). A dollar invested 22 years ago became roughly 27 to 28 times net of fees.
    • Going direct on X, with 2.2 million followers, lets him communicate his vision and lower the friction for others to back his bets, even as his very long tweets have become a running meme.

    Detailed Summary

    From activist trades to permanent capital

    Ackman frames the evolution of his career as a steady move toward business quality. As a smaller, more liquid investor early on, he did not have to think as long-term. As Pershing Square became a bigger, more concentrated investor, durable growth became the dominant factor in every decision. He insists he is still as activist as ever, but a lot of that energy has shifted to X, where he can argue a position publicly rather than only inside a boardroom. The best investments, he notes, are the ones where you do not need to join the board and do anything at all.

    The Wendy’s and Tim Hortons origin story

    One of Pershing Square’s first investments was Wendy’s, which owned the Canadian coffee and donut chain Tim Hortons. The value of Tim Hortons alone was greater than the entire value of Wendy’s, so the idea was simple: buy Wendy’s, spin off Tim Hortons, and double the money. Ackman bought ten percent of the company and could not get the CEO to return a single call, so he had a contact at Blackstone, with Steve Schwarzman’s sign-off, write a fairness opinion on what Wendy’s would be worth after a spin-off, filed it publicly, and watched the spin-off happen six weeks later. The CEO eventually called back to thank him, having been fired but rewarded with a large exit package. Over the years that scrappy approach gave way to a reputation that now opens doors on its own.

    Why a long-term shareholder on the board matters

    The core problem of being a public company, in Ackman’s telling, is the short-term nature of markets and analysts, when a good business should be run in the context of years and even decades. A large, supportive shareholder on the board gives management a place to test ideas before exposing them to the public and a credible voice willing to back initiatives that hurt earnings for a few quarters. That is the value-add he believes a constructive activist can bring to a mature public company, as opposed to a startup where the best outcome is simply to own a great business and stay out of the way.

    AI and the rising risk of disruption

    For a concentrated, long-term investor, the most challenging task is judging the risk that two people from Stanford in a garage build something that destroys your thesis. Ackman argues that risk has climbed dramatically because this is the greatest era in history to build a company, with near-unlimited access to compute, capital, and talent. The paradox is that the conditions that make building easier also make incumbents more fragile, so the bulk of his research now centers on assessing how disruptible a business really is.

    What the market is missing

    Investors bring their attention to the new new thing, currently chips, semiconductors, and energy, which leaves high-quality companies behind. Ackman compares the moment to 2000, when Berkshire Hathaway traded at one of its lowest valuations ever because capital was chasing internet stocks. He sees an echo today in how Amazon, Meta, and Microsoft are treated as old-fashioned, and he considers them undervalued on fundamentals, where value is the present value of the cash a business generates over its life. His recent bullish call, like his March 2020 appearance, came because stocks of really high-quality companies had simply gotten too cheap.

    The SaaS question and AI-enabled software

    On the so-called SaaS apocalypse, Ackman says it is a company-by-company analysis. He worries more about something like Salesforce than about a low-priced platform. The companies most at risk are those that extracted near-monopolistic profits by charging a high annual price for a niche product, because AI lowers the barrier to replicating that functionality. A platform where the average customer pays a small amount per seat, like Microsoft, is far less exposed. The takeaway for any software company is to become as AI-enabled as it possibly can.

    Underwriting SpaceX, xAI, and the AI labs like venture

    For the highest-multiple private companies, Ackman uses a venture lens and a framework a business school professor taught him: people, opportunity, context, deal. SpaceX scores as one of one on people and opportunity, with an incredible context and a near-monopoly in low-cost launch through Starlink, which makes even Amazon a likely customer. The complicated variable is the deal, meaning the valuation, and he admits he has not done all the math, having invested through an SPV after Ron Baron encouraged him, along with a position in xAI. He treats OpenAI, Anthropic, and Palantir as late-stage venture bets that have proven real revenue, and argues OpenAI in particular should communicate more clearly how it justifies capital commitments that vastly exceed current revenue.

    Founder-led companies and the authority to act

    Ackman agrees that founder-led companies have a structural advantage in a fast-changing environment. The average S&P 500 CEO has a tenure of roughly three to four years, a small economic stake, and an incentive not to make a career-ending mistake. A founder is betting an entire life and reputation, has the authority of a major voting and economic position, and has usually made several hard, contrarian calls that turned out right. He points to Mark Zuckerberg’s acquisitions of Instagram and WhatsApp, which looked shocking at the time, as exactly the kind of decision a founder with a track record can make and a hired manager often cannot.

    Howard Hughes as Berkshire Hathaway 2.0

    Ackman points to a detailed financial history of Berkshire Hathaway showing that the vast majority of Buffett’s value creation came from owning insurance and focusing on the asset side of the balance sheet, not just the liability side. Insurance is hard to replicate because skilled investors join hedge funds rather than insurers, but Buffett owned half his company and was a great investor. Pershing Square is applying the same idea to Howard Hughes, a company created out of the General Growth bankruptcy that owns master-planned cities such as Summerlin, with 26,000 acres around Las Vegas, in the spirit of the Irvine Company that made Donald Bren roughly a hundred billion dollars. The plan is to reinvest the company’s cash into insurance, place policyholder float in short-term treasuries and the surplus in common stocks, avoid issuing stock the way Buffett did, and compound for fifty years, all bought at around sixty cents on the dollar.

    Cost of capital, reflexivity, and going direct

    A company only creates value when it earns above its cost of capital, which is why Howard Hughes, seen as a high-cost-of-capital real-estate business, has long traded at a discount, and why Ackman is repurposing its assets into a higher-returning model. He highlights how reflexive markets are: a higher stock price itself makes a company more valuable by lowering its cost of capital and creating currency to raise money and acquire businesses, a lever Elon Musk used to build Tesla. He attributes real market change less to himself and more to figures like Ryan Cohen and GameStop, where personality and a following can lift a stock far above its value. His own going-direct strategy on X, with 2.2 million followers and famously long posts, is the same mechanism applied to communicating a vision and lowering friction for investors. He closes by laying out three ways to invest with Pershing Square: the management company as a royalty on compounding assets, the PSUS portfolio trading at an 18 percent discount, and Howard Hughes as a bet on building the next Berkshire.

    Notable Quotes

    “The best investments are one where you don’t need to join the board and do anything.”

    Bill Ackman, on the kind of business he most wants to own

    “The probability of your being disrupted has gone up enormously.”

    Bill Ackman, on why assessing disruption risk now dominates his research

    “Valuation is like a tether on the market, right? When it gets too high, it’s like this rubber band that’s stretching and inevitably it bounces back.”

    Bill Ackman, on how prices revert at both extremes

    “People, opportunity, context, deal.”

    Bill Ackman, on the business school framework he uses to underwrite companies like SpaceX

    “Every CEO in America today is like, how do I use AI?”

    Bill Ackman, on AI as the top opportunity and threat in every boardroom

    “A closed mouth gathers no foot.”

    Bill Ackman, quoting the line a friend put next to his name in his high school yearbook

    “The increase in value of the company increases the value of the company, right? Because it lowers the cost of capital, it gives you more flexibility, gives you the ability to issue stock, raise capital, acquire other businesses.”

    Bill Ackman, on the reflexivity between stock price and corporate value

    “The company’s got like a $4 billion market cap and the goal is to build it into a trillion dollar thing over time compounding.”

    Bill Ackman, on his fifty-year plan for Howard Hughes

    Taken together, the conversation is a tour of how Ackman now thinks about quality, disruption, and compounding, and a preview of the Berkshire-style machine he wants to build out of Howard Hughes. Watch the full conversation here.

    Related Reading

  • Dan Shipper’s Most Contrarian AI Predictions for 2026: Why the Job Apocalypse Is a Myth, SaaS Will Boom, PMs and Designers Win, and CLIs Are Already Over

    Dan Shipper, the CEO and founder of Every, returned to Lenny’s Podcast for round two of AI predictions. His last appearance produced one of the most prescient calls of the year: that non-technical people would build serious work inside Claude Code. He was unbelievably right. This conversation is the follow-up, a tour of his most contrarian forecasts for how AI is actually changing the way we work, who wins, who loses, and what almost every commentator is getting wrong about the next twelve to twenty-four months.

    TLDW

    Shipper argues that the AI job apocalypse is a myth, that SaaS is going to boom rather than die, that product managers and full-stack designers are the biggest winners of the agent era, that personal agents inside Codex and Claude Code will quietly replace the browser as the primary work surface, that every company will run a single shared super-agent in Slack instead of a fleet of per-user bots, that the CLI moment is already over, that pull requests are going to flood organizations from non-technical staff, that forward-deployed engineers who garden company agents become the new senior role, that GPT-5.5 still cannot match a real senior engineer on architectural judgment, that AI-generated internal writing is fine and probably better than what most humans produce, that CEOs and middle managers have not adapted yet but soon will be forced to, that the edge of AI lives wherever a curious human is using it rather than in San Francisco, and that the only durable strategy is to ride the models and keep playing with whatever ships next. The whole conversation balances aggressive AI bullishness with an equally strong bet on humans, on creativity, and on the unavoidable need for someone to care for every agent that gets deployed.

    Thoughts

    The most useful frame Shipper gives is that models commoditize yesterday’s human competence. Every time a frontier model crosses a new bar, the work that used to define seniority becomes cheap. The senior engineer who could carry a refactor in their head, the PM who could write a coherent strategy doc, the designer who could ship a polished landing page in a week. That competence is now frozen, codified, and available on tap. The interesting question is not whether models will keep eating tasks. They will. The interesting question is what humans do with the suddenly cheap raw material underneath them. Shipper’s answer is that humans climb the stack: they go up a level, find a new problem worth framing, and use the commoditized competence as feedstock for something that did not exist before. That treadmill is the actual engine of value creation, and it is why he can be simultaneously AI pilled and bullish on hiring.

    His SaaS take is the spiciest call of the episode and probably the most defensible. The crowd consensus is that agents will gut SaaS because an AI can just write the form filler, the dashboard, the workflow. Shipper points out the obvious counterfactual: agents do not reduce the number of people using SaaS, they increase it. A marketing lead who could never touch the data warehouse can now stand up a PostHog query through Codex. A founder who never opened Vanta can run a SOC 2 prep through an agent. The result is more users, more accounts, and a much fatter top of funnel for every horizontal tool. The second-order effect is even more interesting. When the SaaS tool runs inside the user’s agent, the user supplies the tokens. Vendor margins improve, not collapse. If he is right, the next two years are going to be brutal for the SaaS-is-dead thesis pieces and very good for the public software multiples.

    The PM and designer bet is where this gets personal for anyone in product. For a decade the bottleneck in shipping anything was engineering capacity. A PM with spiky product sense had to negotiate their vision through a roadmap, a sprint, a review, and a release. Designers had to convince an engineer that the third state of the empty screen was actually worth building. Both of those constraints are dissolving fast. A PM who can prompt Codex into a working prototype on Friday afternoon, then iterate it live in front of a customer on Monday, is doing the job of a small team. A designer who can ship a fully functional landing page in their own style, without negotiating with anyone, is suddenly the most leveraged person in the company. The scarce skill is no longer execution. It is taste, judgment, and the willingness to decide what is worth building. That has always been the real PM and design job. AI just stripped away the parts that were not.

    The quietest but most important prediction is that agents need humans, permanently. Every benchmark advance reveals a new layer of judgment the model cannot frame on its own. When the agent finishes the task, there is always a senior human who sees the deeper problem the model patched over. Shipper calls this gardening, and it is the basis for the new forward-deployed engineer role. The companies winning right now are the ones that put a real person next to every agent, watching what it does, course-correcting in Slack, and noticing when the output drifts. The dream of autonomous AI workflows is a stage in a journey, not the destination. The destination looks more like a thoughtful operator with a small cluster of agents they trust and constantly tend. That is a much more humane future than the discourse suggests, and it is the one Every is already living.

    The final advice, ride the models, sounds glib but is the single most actionable line in the episode. Most professional anxiety about AI dissolves the moment you actually use the newest model on real work. Most professional advantage accrues to the people who do that one thing consistently. The edge does not live in San Francisco where the labs build the things. It lives wherever a curious human meets a real workflow and discovers something the labs have not noticed. A PM in Iowa willing to try Codex on a Tuesday night can be further ahead than a research engineer who has only used the model on its evals. Pair that with Shipper’s closing motto, do things worth writing about and write things worth reading, and you have a pretty complete operating system for the next two years.

    Key Takeaways

    • The AI job apocalypse narrative is wrong. Models commoditize yesterday’s competence, then humans climb the stack and find new work to do with the cheap raw material.
    • Every has roughly doubled headcount in the last year despite being one of the most AI-forward companies in the world. The lived data point cuts directly against the doom thesis.
    • Shipper’s dual stance: simultaneously extremely AI pilled and very bullish on humans. He treats this as the only intellectually honest position right now.
    • Work will bifurcate. Companies will run one shared super-agent in Slack for everyone, and individuals will run their own personal agent inside Codex or Claude Code on their machine.
    • The personal agent inside Codex effectively becomes the new operating system. Instead of putting AI in the browser, you put a browser inside the AI.
    • The super-agent pattern is already real: Shopify has River, Ramp has its own, and Every runs Claudie inside Slack for internal consulting.
    • SaaS is not dying. Agents increase the user base of SaaS tools because non-technical people can finally drive them. Shipper would buy SaaS stocks today.
    • When SaaS runs inside an agent, the user brings their own tokens. Vendor margins improve because they no longer eat inference costs on every interaction.
    • The CLI era is already over. The magic was never the terminal. It was the AI plus the ability to see what the agent is doing. A good GUI captures the same benefits and more.
    • Pull requests are about to flood every company. Non-engineers can now ship code, run queries, and open tickets. Reviewing the output becomes the new bottleneck.
    • Open-source maintainers are already living in the future. Some receive thousands of agent-generated PRs per day and spin up thousands of Codex instances just to triage them.
    • Forward-deployed engineers are the new senior role. They live in Slack, garden the company’s agents, fix broken flows, and keep non-technical staff from doing damage.
    • Product managers with spiky product sense plus a little Codex fluency become extremely dangerous. Marcus at Every, formerly a PM at Axios, is the archetype.
    • Full-stack designers are the other big winner. They can build distinctive interfaces end to end without negotiating with engineering. The bottleneck on taste-driven product work disappears.
    • Designer hiring data has not yet caught up to the prediction. Shipper notes this and says check back in a year.
    • Sales is the role least changed so far. Top of funnel research has been turbocharged by agents, but the actual relationship and closing work remains human.
    • AI-generated internal writing is going mainstream and that is a good thing. Most humans are bad at strategy docs, quarterly plans, and PRs. AI drafts a coherent first pass that a human can refine.
    • Shipper says most of his email is now written by GPT-5.5 and Codex. He would honestly prefer the signature to say so.
    • Public writing, newsletters, and published essays still demand a human voice. Internal communication does not.
    • CEOs and middle managers have largely not adapted yet because their staff still does the work. That window is closing fast and will become an obvious career liability.
    • Your company will only go as far as your CEO goes in AI. The leadership ceiling becomes the AI ceiling.
    • Shipper’s senior engineer benchmark scores GPT-5.5 at roughly 62 out of 100. Real senior engineers sit at 85 to 90. Progress is real, but the gap on architectural judgment remains.
    • Models tend to patch problems locally instead of rewriting from first principles. A senior human still sees the deeper rework that the model avoids.
    • Every uses Notion-based agents to draft quarterly plans. The human edits, approves, and stands behind the output.
    • The hard rule on AI-generated communication: you have to read it and stand behind it before sending it. Pasting unread output is the only true no-no.
    • Every agent needs a human. Automation is a lie in the strong sense. The story of automation is the story of new and different humans being needed alongside it.
    • The reach test, organic daily usage, is the real signal that an AI product works. Benchmark scores are noisy. Daily reach is not.
    • Cursor’s SpaceX acquisition is a tell. Harnesses around models, not the models themselves, are where the strategic value is concentrating.
    • The edge of AI is not in San Francisco. It is wherever a real human meets a real workflow and discovers something the labs have not noticed yet.
    • A PM in Iowa willing to ride the models can be further ahead than a researcher in SF who only uses them on internal evals.
    • Ride the models. Use them for whatever you do. Try every new release the day it ships. That single behavior compounds faster than any other AI career strategy.
    • Shipper got bursitis, which he calls vibe coder elbow, from too much rapid agent-assisted coding while debugging his markdown editor Proof.
    • The closing motto for the year: do things worth writing about and write things worth reading.
    • Lenny will re-interview Shipper in roughly May 2027 to score the predictions.

    Detailed Summary

    Why The AI Job Apocalypse Is The Wrong Frame

    Shipper opens with the headline contrarian call. Benchmarks keep climbing. Models can now sustain seventeen-hour autonomous tasks at fifty percent accuracy. The pace is real and accelerating. None of that translates cleanly into mass unemployment. His mechanism: models codify yesterday’s human competence and make it cheap. The act of compressing past expertise into an API call is genuinely deflationary for the work it captures, but it is also raw material for the next layer of human work. He uses Every as his own data point. The company has roughly doubled in the past year despite being one of the most AI-forward outfits in media. Hiring goes up because agents create new categories of work that need humans, not because the agents fail. The discourse, he argues, is stuck modeling AI as substitution. The reality looks much more like leverage.

    The Bifurcation: Super-Agents And Personal Agents

    Work splits into two surfaces. The first is the shared super-agent that lives in Slack and serves the whole company. Shopify has River. Ramp has its own. Every has Claudie. Each is a single, trusted, gardened agent that anyone in the company can talk to. The pattern has converged on one shared agent rather than one agent per person because agents need human attention to stay useful, and a single shared instance pools the gardening cost. The second surface is the personal agent inside Codex or Claude Code that runs on your machine and reaches into your local environment, your editor, your files, and through an embedded browser into the web. Shipper calls this the new operating system. Instead of the old paradigm of putting AI inside the browser, you put the browser inside the AI. The agent sees what you see, follows what you do, and works on your stuff in your context.

    The SaaS Bet: Up, Not Down

    The SaaS-is-dead thesis was the consensus call of late 2025. Shipper takes the other side and would buy software stocks now. Three arguments. First, agents make SaaS accessible to people who never could have used it directly. The total addressable user base inside every company goes up. Second, the business model improves when the user runs the SaaS through their own agent, because the user supplies the tokens. Vendors stop subsidizing inference. Third, SaaS spend in his observable universe is up, not down, and is concentrating on the tools that play well with agents. He frames the prediction as a sound bite for the cycle: buy SaaS stocks, the apocalypse is dumb.

    The CLI Era Is Already Over

    For a moment in early 2026 it looked like everyone was migrating to the terminal because Claude Code was a CLI. Shipper says the moment is finished. The actual leverage was never the terminal. It was the model plus the ability to watch and steer an agent live. A great GUI captures every advantage of the CLI without the friction. His own engineering team at Every has mostly moved off the CLI as their primary surface and onto Codex desktop. He frames it bluntly: we speed ran the CLI era, it was nice, and now we are done. Tooling for the next two years will be visual, multi-pane, multi-agent, and built around the human watching the work unfold.

    The Pull Request Flood And The Rise Of Forward-Deployed Engineers

    Once non-engineers can ship code, run queries, and file changes through agents, the volume of incoming work explodes. Open-source maintainers already report receiving thousands of agent-generated pull requests per day. Inside companies, the same thing happens to data teams, ops teams, and any function that owns a review gate. The bottleneck shifts from creation to evaluation. The job that emerges to absorb the flood is the forward-deployed engineer. This is a senior person who lives in Slack with the company’s agents, fixes their context, sharpens their instructions, and prevents non-technical colleagues from making well-meaning but incoherent changes. Nitesh at Every is the example Shipper returns to. The model is the same one the labs use internally: pair every important agent with a real engineer who gardens it.

    PMs And Full-Stack Designers Win The Decade

    The two roles Shipper is most bullish on are product manager and full-stack designer. For PMs, the entire job of coordinating a team to translate vision into code collapses into a Codex session. A PM with strong product instincts and a little technical literacy can now prototype, iterate, and even ship. The example is Marcus, formerly a PM at Axios, who took a year to fully internalize AI and now ships faster than most engineers. For designers, the model is similar. The Friday-night-side-project designer who used to be stuck explaining a vision can now build the vision themselves, with their own taste fully expressed. The scarce skill in both cases is the same: judgment about what to build and the courage to decide it is good. Execution capacity is no longer the constraint.

    The Senior Engineer Benchmark And What Models Still Miss

    Shipper has built his own benchmark to test whether coding models can actually do senior engineering work. GPT-5.5 scores around 62 out of 100. Real senior engineers sit closer to 85 or 90. The gap is not in syntax or test pass rates. It is in the willingness to step back, see that a piece of code is fundamentally the wrong shape, and rewrite it from first principles. Models almost universally patch locally. They take the instruction at face value, accept the existing code as a constraint, and optimize within it. A real senior engineer ignores the prompt when the prompt is wrong. This is the durable moat for senior technical judgment, and Shipper expects it to remain visible for at least another year of model releases.

    AI-Generated Writing Goes Mainstream

    Internal writing inside companies is quietly becoming AI-first and Shipper thinks it should. Quarterly plans, status updates, PR descriptions, strategy memos, recruiting outreach, most internal email. He runs his own inbox through GPT-5.5 and Codex and says he would honestly prefer if the recipient knew. The point is not that AI is a better writer in some absolute sense. The point is that most humans are not very good at these specific genres, and the model produces a coherent, structurally sound first draft that a human can guide and approve. The constraint is honesty: you read it, you understand it, you stand behind it. Public writing, like the newsletters Every publishes, still demands a human voice. Internal communication does not, and treating it as if it did is a tax on the organization.

    The CEO And Middle Manager Lag

    Shipper points to a population that has largely escaped AI adoption: senior leaders and middle managers. They have staff to do the work, so they have not been forced to pick up the tools personally. He thinks this is the single largest pocket of latent disruption coming in the next year. Your company will only go as far as your CEO goes in AI, because every decision about where to deploy agents, where to hire, and how to restructure work flows downstream from leadership taste. A leader who has not personally lived inside Codex or Claude Code for a few weeks cannot make those calls well. Expect this to flip fast and to become a visible career liability for executives who do not adapt.

    Ride The Models

    The closing advice is the simplest. Ride the models. Use AI for whatever you actually do. Try every new release the day it lands. Most of the professional anxiety around AI dissolves on contact with the work, and most of the durable advantage in the field belongs to the people who do this one thing consistently. Shipper notes that the edge of AI does not live in San Francisco. It lives wherever a curious operator meets a real workflow and notices something nobody at the labs has yet. A PM in Iowa willing to spend a Tuesday night exploring Codex can find capabilities researchers have not surfaced. Pair that with his motto, do things worth writing about and write things worth reading, and you have most of an operating system for the next two years.

    Notable Quotes

    “The AI job apocalypse is not really a thing. I am super super bullish on PMs and full-stack designers.”

    Dan Shipper, opening his contrarian thesis for the conversation

    “I’m simultaneously extremely AI pilled and very bullish on humans. Automation is a lie. Every agent needs a human.”

    Dan Shipper, on holding both sides of the AI debate at once

    “What models do in general is they make yesterday’s human competence cheap. And so, it becomes commoditized. It’s not valuable anymore. What humans do is we go in there and we’re like, yeah, we have all this frozen human competence from yesterday, how do I use this to make something new and interesting.”

    Dan Shipper, articulating the core engine behind his anti-apocalypse thesis

    “I would buy SaaS stocks right now. The SaaS apocalypse is dumb. What agents do is increase the number of users of SaaS, not get rid of it.”

    Dan Shipper, calling the consensus SaaS-is-dead thesis directly wrong

    “We speed ran the CLI era. It was nice while it lasted, but I think CLIs are over.”

    Dan Shipper, on why the terminal-first agent moment is already done

    “Most of my email is written by GPT-5.5 and Codex right now. And I honestly would prefer it to say that it’s coming from GPT-5.5.”

    Dan Shipper, on the new etiquette of AI-assisted communication

    “The edge of AI is not in San Francisco. The edge of AI is wherever AI meets a real human doing something.”

    Dan Shipper, on where the actual frontier of the field lives

    “The only thing you need to do is ride the models. And that means use them for whatever it is that you do.”

    Dan Shipper, distilling his career advice for the next two years

    “Do things worth writing about and write things worth reading.”

    Dan Shipper’s closing motto, lifted from his own operating system at Every

    Watch the full conversation with Dan Shipper on Lenny’s Podcast here. The re-interview to score these predictions is scheduled for roughly May 2027.

    Related Reading

    • Every. Dan Shipper’s company and the live laboratory for almost every prediction in this conversation, including Spiral, Cora, and Claudie.
    • The Allocation Economy by Dan Shipper. The earlier essay that frames humans as managers of AI labor and underpins much of the gardening-the-agent thesis here.
    • Claude Code by Anthropic. The agent surface Shipper called correctly last year and one of the two environments he predicts will become the new operating system for work.
    • Codex by OpenAI. Shipper’s current daily driver and the visual, multi-pane agent environment he uses for almost everything from coding to email.
    • The Writing Life by Annie Dillard. The book Shipper makes every Every employee read, and the source of the company’s stance on writing as a tool for noticing the future.